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Subdistribution Regression for Recurrent Events Under Competing Risks: with Application to Shunt Thrombosis Study in Dialysis Patients

Author

Listed:
  • Chia-Hui Huang

    (National Taipei University)

  • Bowen Li

    (KKBOX Taiwan Co)

  • Chyong-Mei Chen

    (School of Medicine, National Yang-Ming University)

  • Weijing Wang

    (National Chiao Tung University)

  • Yi-Hau Chen

    (Academia Sinica)

Abstract

This work is motivated by a nephrology study in Taiwan, where, after shunt implantation, dialysis patients may experience one of the two types, acute and non-acute, of shunt thrombosis, and each of them may alternatively recur in a patient. In this work, treating the two types of shunt thrombosis as competing risks, we assess covariate effects on the cumulative incidence probability function, or subdistribution, of gap times to the occurrences of acute shunt thrombosis. To accommodate potentially time-varying covariate effects, we extend a varying-coefficient subdistribution regression model to recurrent event analysis and propose associated estimation procedures. The inverse probability of censoring weighting technique is employed to ensure consistent estimation of the regression parameter. Asymptotic distributional theory is derived for the proposed estimator. Simulation results confirm that the proposed estimator performs well in finite samples. Application of the proposed analysis to the shunt thrombosis data reveals that dialysis patients with graft shunts and hypertension are associated with significantly increased incidence of acute shunt thrombosis.

Suggested Citation

  • Chia-Hui Huang & Bowen Li & Chyong-Mei Chen & Weijing Wang & Yi-Hau Chen, 2017. "Subdistribution Regression for Recurrent Events Under Competing Risks: with Application to Shunt Thrombosis Study in Dialysis Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 339-356, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9161-0
    DOI: 10.1007/s12561-016-9161-0
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    References listed on IDEAS

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    1. Douglas E. Schaubel, 2004. "Regression methods for gap time hazard functions of sequentially ordered multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(2), pages 291-303, June.
    2. Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    3. Yu Cheng & Jason P. Fine, 2012. "Cumulative incidence association models for bivariate competing risks data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 183-202, March.
    4. Donglin Zeng & D. Y. Lin, 2006. "Efficient estimation of semiparametric transformation models for counting processes," Biometrika, Biometrika Trust, vol. 93(3), pages 627-640, September.
    5. Wang M-C. & Qin J. & Chiang C-T., 2001. "Analyzing Recurrent Event Data With Informative Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1057-1065, September.
    6. Xuelin Huang & Lei Liu, 2007. "A Joint Frailty Model for Survival and Gap Times Between Recurrent Events," Biometrics, The International Biometric Society, vol. 63(2), pages 389-397, June.
    7. Douglas Schaubel & Jianwen Cai, 2006. "Rate/Mean Regression for Multiple‐Sequence Recurrent Event Data with Missing Event Category," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 191-207, June.
    8. J. P. Fine, 1999. "Analysing competing risks data with transformation models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 817-830.
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    Cited by:

    1. Lo, Simon M.S. & Mammen, Enno & Wilke, Ralf A., 2020. "A nested copula duration model for competing risks with multiple spells," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

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